The present invention relates 3D object detection in images, and more particularly, to automated Ileo-Cecal Valve (ICV) detection in colon CT data using incremental parameter learning.
Detecting and segmenting human anatomic structures in 3D medical image volumes (e.g., CT, MRI, etc.) is a challenging problem, which is typically more difficult than detecting anatomic structures in 2D images. Human anatomic structures are highly deformable by nature, leading to large intra-class variation in the shape, appearance, and pose (orientation) of such structures in 3D medical images. Furthermore, the pose of an anatomic structure is typically unknown in advance of detection. If the pose of an anatomic structure were known prior to detection, it would be possible to train a model for the same category of anatomic structure with a fixed pose specification and pre-align all testing data with the known pose information to evaluate their fitness against the learned model. However, in order to determine the pose configuration of an anatomic structure, the structure itself must be first detected, because pose estimation is only meaningful where the structure exists. Accordingly, a method for simultaneous detection and registration of 3D anatomic structures is need.
Many three dimensional (3D) detection and segmentation problems are confronted with searching in a high dimensional space. For example, a 3D similarity transformation is characterized by nine parameters: three position parameters, three orientation parameters, and three scale parameters. It is very expensive to search the entire space for detection of an object. The search for all these parameters becomes computationally prohibitive, even if coarse-to-fine strategies are involved.
The Ileo-Cecal Valve (ICV) is a small anatomic structure connecting the small and large intestines in the human body. The normal functionality of the ICV (opening and closing on demand) allows food to pass into the large intestine (i.e., colon) from the small intestine. The ICV being stuck in either the open or closed position can cause serious medical consequences. Furthermore, detecting the ICV in 3D computed tomography (CT) volumes is important for accurate colon segmentation and for distinguishing false positives from polyps in colon cancer diagnosis. The size of the ICV is sensitive to the weight of the patient and whether the ICV is healthy or diseased. Because the ICV is part of the colon, which is highly deformable, the position and orientation of the ICV can vary greatly. Due to large variations in the position, size, and orientation of the ICV, detecting the ICV in CT volumes can be very difficult. Accordingly, a method for automatically detecting the size, position, and orientation of the ICV is needed.